Sustainability of a Primary Care–Driven eConsult Service
Bibliographic record
Abstract
PURPOSE: Excessive wait times for specialist appointments pose a serious barrier to patient care. To improve access to specialist care and reduce wait times, we launched the Champlain BASE (Building Access to Specialists through eConsultation) eConsult service in April 2011. The objective of this study is to report on the impact of our multiple specialty eConsult service during the first 5 years of use after implementation, with a focus on growth and sustainability. METHODS: We conducted a cross-sectional study of all eConsult cases submitted between April 1, 2011 and April 30, 2016, and measured impact with system utilization data and mandatory close-out surveys completed at the end of each eConsult. Impact indicators included time interval to obtain specialist advice, effect of specialist advice on the primary care clinician's course of action, and rate of avoidance of face-to-face visits. RESULTS: A total of 14,105 eConsult cases were directed to 56 different medical specialty groups, completed with a median response time of 21 hours, and 65% of all eConsults were resolved without a specialist visit. We observed rapid growth in the use of eConsult during the study period: 5 years after implementation the system was in use by 1,020 primary care clinicians, with more than 700 consultations taking place per month. CONCLUSIONS: This study presents the first in-depth look at the growth and sustainability of the multispecialty eConsult service. The results show the positive impact of an eConsult service and can inform other regions interested in implementing similar systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".